Using means and standard deviations of statistical forecasts has been the default method for demand planners for decades, but there is a key shortcoming with this approach, namely that it assumes that demand is normally distributed, which it rarely is. This incorrect assumption severely impacts forecast accuracy and accuracy of all dependent plans. The solution, and an increasingly adopted method, is probabilistic forecasting. In this article I discuss how probability distributions allow planners to work with the real uncertainty in demand and enjoy more accurate demand plans as a result. I also explore other benefits of this approach and the differences between deterministic and probabilistic forecasting.

From Issue: Special Issue: Technology In Forecasting & Planning
(Winter 2019-2020)